1 August 1990 3D surface reconstruction from contour line image by a regularization method
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Proceedings Volume 1395, Close-Range Photogrammetry Meets Machine Vision; 13950U (1990) https://doi.org/10.1117/12.2294273
Event: Close-Range Photogrammetry Meets Machine Vision, 1990, Zurich, Switzerland
Abstract
The regularization method, which is performed by minimizing an energy functional of the image, has recently been applied to many ill-posed problems in computer vision. Notably, Grimson(1983) developed a regularization method for surface reconstruction which used a sparse set of known elevation data. We have developed an approach to surface reconstruction using both contour image data and a sparse set of known elevation values. We define a new energy functional which integrates three kinds of constraints : smoothness, fitness, and contour line constraint. These constraints seek to ensure that the reconstructed surface smoothly approximates the known elevation values and has the same height value for all points on a contour line. The energy functional can be minimized by solving a large linear system of simultaneous equations. We have successfully reconstructed a detailed 3D topography by applying this method to contour lines and known sparse elevation data extracted from moire images and topographic maps.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shigeru Muraki, Naokazu Yokoya, Kazuhiko Yamamoto, "3D surface reconstruction from contour line image by a regularization method", Proc. SPIE 1395, Close-Range Photogrammetry Meets Machine Vision, 13950U (1 August 1990); doi: 10.1117/12.2294273; https://doi.org/10.1117/12.2294273
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